In the online space, content publishers are often tasked with making decisions for allocating and serving content to clients in connection with presentation opportunities. One approach a content publisher may utilize for these decisions is an auction-based content delivery system where bids are made on bid targets, such as key words, to rank and select content for the presentation opportunities. Content may be displayed in a particular order or pattern or otherwise be selected for placement in a webpage or other presentation medium based on the bids. Higher ranked content is typically more prominently displayed to clients on client devices.
Bidders can use bid management tools, such as Adobe® Media Optimizer to assist in determining how much to bid on bid targets in content delivery auctions. These tools may assist users by predicting the performance of bid targets based on historical observations of their performance in a content delivery auction. Predictions for a bid target may be more accurate when they factor in the time of day or other context of presenting content in the content delivery auction. Typically, a dedicated model is used to predict performance of a bid target with respect to a particular set of context factors. Thus, an entirely new model with associated storage and processing is required to predict performance for the bid target with respect to at least one different context factor, or the same context factors for a different bid target. Additionally, this approach is prone to providing unreliable predictions because the number of historical observations that match both the bid target and each context factor being predicted by a model may be sparse, especially for a large set of context factors.